Implementable Algorithm for Stochastic Optimization Using Sample Average Approximations
Authors
Royset, J.O.
Polak, E.
Subjects
Stochastic optimization
sample average approximations
Monte Carlo simulations
reliability-based optimal designs
sample average approximations
Monte Carlo simulations
reliability-based optimal designs
Advisors
Date of Issue
2004-07
Date
July 2004
Publisher
Plenum Publishing Corporation
Language
Abstract
We develop an implementable algorithm for stochastic
optimization problems involving probability functions. Such problems
arise in the design of structural and mechanical systems. The algorithm
consists of a nonlinear optimization algorithm applied to sample
average approximations and a precision-adjustment rule. The sample
average approximations are constructed using Monte Carlo simulations
or importance sampling techniques. We prove that the algorithm
converges to a solution with probability one and illustrate its use by
an example involving a reliability-based optimal design.
Type
Article
Description
Series/Report No
Department
Operations Research (OR)
Organization
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
Sponsors
Research Associateship Program at the National Research Council
Taisei Chair in Civil Engineering at UC Berkeley
National Science Foundation under Grant ECS-9900985
Taisei Chair in Civil Engineering at UC Berkeley
National Science Foundation under Grant ECS-9900985
Funder
Research Associateship Program at the National Research Council
Taisei Chair in Civil Engineering at UC Berkeley
National Science Foundation under Grant ECS-9900985
Taisei Chair in Civil Engineering at UC Berkeley
National Science Foundation under Grant ECS-9900985
Format
28 p.
Citation
J.O. Royset and E. Polak, 2004, “Implementable Algorithm for Stochastic Optimization using Sample Average Approximations,” Journal of Optimization Theory and Applications, Vol. 122, No. 1, pp. 157-184.
Distribution Statement
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.